T-wave oversensing

ABSTRACT

The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data to determine whether T-wave oversensing has occurred. The T-wave oversensing analysis may include, for example, identifying beat runs within the cardiac episode whether the beats within the run have at least one characteristic that alternates beat to be or clustering beats within the cardiac episode based on beat to beat interval length. The T-wave oversensing determination may be based on probabilistic analysis in some examples.

TECHNICAL FIELD

The disclosure relates to algorithms for classifying cardiac episodesdetected by an implantable medical device (IMD).

BACKGROUND

Some implantable medical devices (IMDs) monitor physiological parametersor signals of the patients within which they are implanted. Suchimplantable medical devices may detect episodes based on the monitoring.An IMD may store a variety of data regarding detected episodes, and aclinician may retrieve the episode data from the IMD for diagnosing thepatient and/or confirming the accuracy of the detection of the episodesby the IMD. For example, implantable cardioverter-defibrillators (ICDs)may detect cardiac episodes, such as tachyarrhythmia episodes, based onmonitoring cardiac electrogram signals and, in some cases, additionalphysiological signals or parameters. A clinician may review the datastored by the ICD for the episodes to confirm that accuracy of thediagnosis of tachyarrhythmia by the ICD.

As the memory capacity and diagnostic capabilities of IMDs, such asICDs, increases, the amount of time required to adequately review theretrieved data to determine whether the detection of episodes anddelivery of therapy by the device was appropriate also increases. Manualreview of episodes may be challenging because of the number of patientsa clinician follows, an increase in the total number of episodes toreview and the significant level of expertise required. Additionally,the time available for clinicians with expertise to review each episodehas been reduced. This may result in a reduction in the quality ofmanagement of those patients having implanted devices.

Automated algorithms for post-processing cardiac episodes previouslydetected by ICDs have been proposed to address these concerns. Suchalgorithms generally evaluate the cardiac electrogram and other datastored by an ICD for an episode to provide an independent classificationof the episode. The post-processing classification may be compared tothe classification made by the ICD to determine the accuracy of theclassification by the ICD. Such algorithms may potentially suggest ICDparameter changes and/or changes to medical therapy, such as changes inmedication, therapy delivery, use of ablation procedures, etc. Onealgorithm for automated algorithms for post-processing of cardiacepisodes is disclosed in U.S. Pat. No. 7,894,883 to Gunderson et al.,which is incorporated herein by reference in its entirety.

SUMMARY

In general, the disclosure is directed to an episode classificationalgorithm for classifying cardiac episodes. In some examples, theepisode classification algorithm includes a determination of whetherT-wave oversensing is present in the cardiac episode.

In one example, the disclosure is directed to a method for determiningwhether T-wave over-sensing (TWOS) occurred during a cardiac episodecomprising a plurality of sensed beats. The method includes identifyingat least one beat run of at least a predetermined number of consecutiveones of the beats during the episode, wherein each of the beats withinthe run have at least one characteristic that alternates from beat tobeat, clustering the beats into two or more clusters based onbeat-to-beat interval length, and determining, based on at least one ofthe runs and the clusters, whether TWOS occurred during the cardiacepisode.

In another example, the disclosure is directed to a system fordetermining whether T-wave over-sensing (TWOS) occurred during a cardiacepisode comprising a plurality of sensed beats. The system includes aprocessor configured to identify at least one beat run of at least apredetermined number of consecutive ones of the beats during theepisode, wherein each of the beats within the run have at least onecharacteristic that alternates from beat to beat; cluster the beats intotwo or more clusters based on beat-to-beat interval length; anddetermine, based on at least one of the runs and the clusters, whetherTWOS occurred during the cardiac episode.

In another example, the disclosure is directed to a computer-readablemedium containing instructions. The instructions cause a programmableprocessor to identify at least one beat run of at least a predeterminednumber of consecutive ones of the beats during the episode, wherein eachof the beats within the run have at least one characteristic thatalternates from beat to beat; cluster the beats into two or moreclusters based on beat-to-beat interval length; and determine, based onat least one of the runs and the clusters, whether TWOS occurred duringthe cardiac episode.

In another example, the disclosure is directed to a system fordetermining whether T-wave over-sensing (TWOS) occurred during a cardiacepisode comprising a plurality of sensed beats. The system includesmeans for identifying at least one beat run of at least a predeterminednumber of consecutive ones of the beats during the episode, wherein eachof the beats within the run have at least one characteristic thatalternates from beat to beat; means clustering the beats into two ormore clusters based on beat-to-beat interval length; and meansdetermining, based on at least one of the runs and the clusters, whetherTWOS occurred during the cardiac episode.

In another example, the disclosure is directed to a system fordetermining whether T-wave over-sensing (TWOS) occurred during a cardiacepisode comprising a plurality of sensed beats. The system includes aprocessor configured to identify at least one beat run of at least apredetermined number of consecutive ones of the beats during theepisode, wherein each of the beats within the run have at least onecharacteristic that alternates from beat to beat, and determine, basedon the at least one beat run whether TWOS occurred during the cardiacepisode.

In another example, the disclosure is directed to a system fordetermining whether T-wave over-sensing (TWOS) occurred during a cardiacepisode comprising a plurality of sensed beats. The system including aprocessor configured to cluster the beats into two or more clustersbased on beat-to-beat interval length, and determine, based on theclusters, whether TWOS occurred during the cardiac episode.

The details of one or more examples consistent with the presentdisclosure are set forth in the accompanying drawings and thedescription below. Other features, objects, and advantages of theinvention will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example system formonitoring and treating cardiac events and analyzing the effectivenessof an IMD.

FIG. 2 is a conceptual diagram illustrating the IMD and leads of thesystem of FIG. 1 in greater detail.

FIG. 3 is a block diagram illustrating an example IMD that monitorscardiac electrogram (EGM) signals and classifies abnormal signals beforeproviding a therapeutic response.

FIG. 4 is a block diagram illustrating an example external programmershown in FIG. 1.

FIG. 5 is a block diagram illustrating an example system that includesan external device, such as a server, and one or more computing devicesthat are coupled to the IMD and programmer shown in FIG. 1 via anetwork.

FIG. 6 is a flow diagram illustrating an example arrhythmia analysissequence implemented by an arrhythmia analyzer.

FIG. 7 is a flow diagram illustrating an example method of determiningthe presence of ventricular over-sensing (VOS) using probabilisticanalysis.

FIG. 8 is a flow diagram illustrating an example method of determiningwhether T-wave over-sensing (TWOS) is present in an EGM signal detectedby an IMD.

FIG. 9 illustrates an example EGM signal and marker channel withcharacteristics used to detect TWOS.

FIG. 10 is a flow diagram illustrating an example method of categorizinga cardiac episode including atrial sensing issues.

FIG. 11 is a flow diagram illustrating an example method of classifyinga cardiac episode as including atrial fibrillation (AF).

FIG. 12 illustrates an example EGM signal and marker channel showing AFcharacteristics.

FIG. 13 is a flow diagram illustrating an example method of classifyinga high-rate rhythm arising immediately after pacing.

DETAILED DESCRIPTION

This disclosure describes techniques for classifying cardiac episodes.In particular, the disclosure describes techniques for identifyingcharacteristics in an EGM signal that may lead to an IMD misclassifyingan episode. In some examples, the techniques are implemented by eitheran IMD or by an external device to evaluate a prior classification of anepisode by the IMD.

In general, an IMD transmits electrogram (EGM) signal data or other dataassociated with a cardiac episode diagnosed by the IMD to an externalcomputing device. In some examples the data is transmitted after theepisode is over. In some examples, data for one or more episodes istransmitted at predetermined intervals. The data stored by an IMD for acardiac episode diagnosed by the IMD may include the diagnosis made bythe IMD and data leading up to diagnosis of the particular cardiacepisode. In some examples, IMD may include episodes resulting in eitheranti-tachycardia pacing or a shock in response to a diagnosis of eitherventricular tachycardia or ventricular fibrillation. It is also possiblethat the IMD may have misdiagnosed a supraventricular tachycardia (SVT),such as sinus tachycardia or an atrial arrhythmia, or noise as atreatable, e.g., shockable, episode.

In some examples, an external computing device analyzes the EGM signalthat was previously used by the IMD to classify an episode, andgenerates its own classification of the episode based on the EGM signal.In some examples, the external device determines whether theclassification of the episode by the IMD was correct by comparing itsclassification of the episode to that of the IMD. The techniquesdescribed below may reduce the number of episodes that the externaldevice is unable to classify with a reasonable degree of confidence.

In some examples, a post-processing classification algorithm may reducethe number of EGM episodes that are unable to be classified confidentlyemploying a probabilistic determination of VOS. The use of aprobabilistic determination of VOS allows for classification of episodesthat may have previously been categorized as indeterminate. As part ofthe probabilistic determination of VOS algorithm, a post-processingclassification algorithm may also use a probabilistic determination ofTWOS. This again increases the number of episodes properly classified ashaving sensing issues and decreases the number of episodes categorizedas indeterminate.

When using either probabilistic detection of VOS or probabilisticdetection of TWOS, an algorithm may look at a number of factors, none ofwhich may be dispositive. However, the algorithm assigns various weightsto each factor for or against the presence of the particularover-sensing issue. After all the factors have been studied, theevidence for and against a particular sensing issue is summed andcompared. In general, if there is more evidence for oversensing, byweight and not necessarily the number of factors themselves, then thealgorithm determines that the episode includes oversensing whichinterfered with the proper classification of the rhythm by theimplantable medical device. In some examples, the presence ofoversensing may result in changes to one or more parameters used by theIMD to diagnosis arrhythmias.

In some examples, a post-processing classification algorithm may reducethe number of EGM episodes that are unable to be classified confidentlyby determining if the EGM signal indicates the presence of atrialsensing issues. The algorithm may determine if the sensing issues arecorrectable. If the sensing issues are not correctable, classificationrules that do not rely on atrial sensing may be used to classify theepisode.

In some examples, a post-processing classification algorithm may reducethe number of EGM episodes that are unable to be classified confidentlyby determining if the EGM signal indicates the presence of atrialfibrillation (AF). The algorithm may look at a number of characteristicsof the EGM signal that may be evidence of AF. Based on the all theevidence a determination is made as to whether or not it is likely theepisode is AF.

In some examples, a post-processing classification algorithm may reducethe number of EGM episodes that are unable to be classified confidentlyby determining whether an episode was properly diagnosed in the presenceof pacing. This algorithm may allow for an increase in the types ofepisodes that may be classified during post-processing. For examples,episodes during cardiovascular resynchronization therapy (CRT) may beclassified, despite the presence of pacing pulses.

Overall, the various algorithms discussed in this disclosure have beenfound to reduce the number of indeterminate classifications by 92% whilealso reducing the number of misclassifications by 24%. In addition, thealgorithms improve tolerance of atrial sensing issues by 64% whileincreasing correct classification of episodes with atrial sensing issueby 50 to 62%.

FIG. 1 is a conceptual diagram illustrating an example system 10 formonitoring and treating cardiac episodes and analyzing the effectivenessof an implantable medical device (IMD) 16. As illustrated in FIG. 1, asystem for monitoring and treating cardiac episodes and providing asummary of the episodes to an external device for review includes an IMD16, such as an implantable cardiac pacemaker, implantablecardioverter/defibrillator (ICD), orpacemaker/cardioverter/defibrillator, for example. IMD 16 is connectedto leads 18, 20 and 22 and is communicatively coupled to a programmer24. IMD 16 senses electrical signal attendant to the depolarization andrepolarization of heart 12, e.g., a cardiac electrogram (EGM), viaelectrodes on one or more leads 18, 20 and 22 or the housing of IMD 16.IMD 16 may also deliver therapy in the form of electrical signals toheart 12 via electrodes located on one or more leads 18, 20 and 22 or ahousing of IMD 16, the therapy may be pacing, cardioversion and/ordefibrillation pulses. IMD 16 may monitor EGM signals collected byelectrodes on leads 18, 20 or 22, and based on the EGM signal diagnosisand treat cardiac episodes. Programmer 24 may receive and summarize theEGM signal based diagnosis and treatment of cardiac episodes provided byIMD 16. The system for summarizing and displaying information regardingdiagnosis and treatment may also be used with other medical devices,such as a cardiomyostimulator, a drug delivery system, cardiac and otherphysiological monitors.

Leads 18, 20, 22 extend into the heart 12 of patient 14 to senseelectrical activity of heart 12 and/or deliver electrical stimulation toheart 12. In the example shown in FIG. 1, right ventricular (RV) lead 18extends through one or more veins (not shown), the superior vena cava(not shown), and right atrium 26, and into right ventricle 28. Leftventricular (LV) coronary sinus lead 20 extends through one or moreveins, the vena cava, right atrium 26, and into the coronary sinus 30 toa region adjacent to the free wall of left ventricle 32 of heart 12.Right atrial (RA) lead 22 extends through one or more veins and the venacava, and into the right atrium 26 of heart 12.

In some examples, programmer 24 takes the form of a handheld computingdevice, computer workstation or networked computing device that includesa user interface for presenting information to and receiving input froma user. A user, such as a physician, technician, surgeon,electro-physiologist, or other clinician, may interact with programmer24 to retrieve physiological or diagnostic information from IMD 16.Programmer 24 may provide to the user a summary of physiological anddiagnostic information for patient 12 over a period of time. A user mayalso interact with programmer 24 to program IMD 16, e.g., select valuesfor operational parameters of the IMD. Programmer 24 may include aprocessor configured to evaluate EGM signals transmitted from IMD 16 toprogrammer 24. In some examples, programmer 24 may evaluate a priorclassification of an episode by IMD 16.

IMD 16 and programmer 24 may communicate via wireless communicationusing any techniques known in the art. Examples of communicationtechniques may include, for example, low frequency or radiofrequency(RF) telemetry. Other techniques are also contemplated. In someexamples, programmer 24 may include a programming head that may beplaced proximate to the patient's body near the IMD 16 implant site inorder to improve the quality or security of communication between IMD 16and programmer 24. In some examples, programmer 24 may be locatedremotely from IMD 16, and communicate with IMD 16 via a network.Programmer 24 may also communicate with one or more other externaldevices using a number of known communication techniques, both wired andwireless.

In some examples, data acquired by IMD 16 can be monitored by anexternal system, such as the programmer 24. Programmer 24 may analyzecharacteristics of EGM signals data corresponding to cardiac episodesrecognized by IMD 16. Arrhythmia analysis of cardiac episodes accordingto an example of the present disclosure may take place in the programmer24 once the required data is transmitted from IMD 16 to the programmer24. In some examples, programmer 24 may transmit the required data toanother external device, not shown in FIG. 1, for analysis.

FIG. 2 is a conceptual diagram illustrating IMD 16 and leads 18, 20 and22 of system 10 in greater detail. In the illustrated example, bipolarelectrodes 40 and 42 are located adjacent to a distal end of lead 18. Inaddition, bipolar electrodes 44 and 46 are located adjacent to a distalend of lead 20, and bipolar electrodes 48 and 50 are located adjacent toa distal end of lead 22. In alternative embodiments, not shown in FIG.2, one or more of leads 18, 20 and 22, e.g., left-ventricular lead 20,may include quadrapole electrodes located adjacent to a distal end ofthe lead.

In the illustrated example, electrodes 40, 44 and 48 take the form ofring electrodes, and electrodes 42, 46 and 50 may take the form ofextendable helix tip electrodes mounted retractably within insulativeelectrode heads 52, 54 and 56, respectively. Leads 18, 20, 22 alsoinclude elongated electrodes 62, 64, 66, respectively, which may takethe form of a coil. In some examples, each of electrodes 40, 42, 44, 46,48, 50, 62, 64 and 66 is electrically coupled to a respective conductorwithin the lead body of its associated lead 18, 20, 22 and therebycoupled to circuitry within IMD 16.

In some examples, IMD 16 includes one or more housing electrodes, suchas housing electrode 4 illustrated in FIG. 2, which may be formedintegrally with an outer surface of hermetically-sealed housing 8 of IMD16 or otherwise coupled to housing 8. In some examples, housingelectrode 4 is defined by an uninsulated portion of an outward facingportion of housing 8 of IMD 16. Other divisions between insulated anduninsulated portions of housing 8 may be employed to define two or morehousing electrodes. In some examples, a housing electrode comprisessubstantially all of housing 8.

Housing 8 encloses a signal generator that generates therapeuticstimulation, such as cardiac pacing, cardioversion and defibrillationpulses, as well as a sensing module for sensing electrical signalsattendant to the depolarization and repolarization of heart 12. Housing8 may also enclose a memory for storing the sensed electrical signals.Housing 8 may also enclose a telemetry module for communication betweenIMD 16 and programmer 24.

IMD 16 senses electrical signals attendant to the depolarization andrepolarization of heart 12 via electrodes 4, 40, 42, 44, 46, 48, 50, 62,64 and 66. IMD 16 may sense such electrical signals via any bipolarcombination of electrodes 40, 42, 44, 46, 48, 50, 62, 64 and 66.Furthermore, any of the electrodes 40, 42, 44, 46, 48, 50, 62, 64 and 66may be used for unipolar sensing in combination with housing electrode4.

The illustrated numbers and configurations of leads 18, 20 and 22 andelectrodes are merely examples. Other configurations, i.e., number andposition of leads and electrodes, are possible. In some examples, system10 may include an additional lead or lead segment having one or moreelectrodes positioned at different locations in the cardiovascularsystem for sensing and/or delivering therapy to patient 14. For example,instead of or in addition to intercardiac leads 18, 20 and 22, system 10may include one or more epicardial or subcutaneous leads not positionedwithin the heart.

FIG. 3 is a block diagram illustrating an example IMD 16 that monitorsEGM signals and classifies abnormal signals before providing atherapeutic response. In the illustrated example, IMD 16 includes aprocessor 70, memory 72, signal generator 74, sensing module 76,telemetry module 78, episode classifier 80, and activity sensor 82.Memory 72 includes computer-readable instructions that, when executed byprocessor 70, cause IMD 16 and processor 70 to perform various functionsattributed to IMD 16 and processor 70 herein. Memory 72 may include anyvolatile, non-volatile, magnetic, optical, or electrical media, such asa random access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory,or any other digital or analog media.

Processor 70 may include any one or more of a microprocessor, acontroller, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), orequivalent discrete or analog logic circuitry. In some examples,processor 70 may include multiple components, such as any combination ofone or more microprocessors, one or more controllers, one or more DSPs,one or more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry. The functions attributed to processor 70herein may be embodied as software, firmware, hardware or anycombination thereof. Generally, processor 70 controls signal generator74 to deliver stimulation therapy to heart 12 of patient 14 according toa selected one or more of therapy programs or parameters, which may bestored in memory 72. As an example, processor 70 may control signalgenerator 74 to deliver electrical pulses with the amplitudes, pulsewidths, frequency, or electrode polarities specified by the selected oneor more therapy programs or parameters. Processor 70 may modify theelectrical pulses delivered by signal generator 74 based on a diagnosisor classification of an EGM signal by episode classifier 80.

Signal generator 74 is configured to generate and deliver electricalstimulation therapy to patient 14. As shown in FIG. 3, signal generator74 is electrically coupled to electrodes 4, 40, 42, 44, 46, 48, 50, 62,64 and 66, e.g., via conductors of the respective leads 18, 20, and 22and, in the case of housing electrode 4, within housing 8. For example,signal generator 74 may deliver pacing, defibrillation or cardioversionpulses to heart 12 via at least two of electrodes 4, 40, 42, 44, 46, 48,50, 62, 64 and 66. In some examples, signal generator 74 deliversstimulation in the form of signals other than pulses such as sine waves,square waves, or other substantially continuous time signals.

Signal generator 74 may include a switch module (not shown) andprocessor 70 may use the switch module to select, e.g., via adata/address bus, which of the available electrodes are used to deliverthe electrical stimulation. The switch module may include a switcharray, switch matrix, multiplexer, or any other type of switching devicesuitable to selectively couple stimulation energy to selectedelectrodes. Electrical sensing module 76 monitors electrical cardiacsignals from any combination of electrodes 4, 40, 42, 44, 46 48, 50, 62,64, and 66. Sensing module 76 may also include a switch module whichprocessor 70 controls to select which of the available electrodes areused to sense the heart activity, depending upon which electrodecombination is used in the current sensing configuration.

Sensing module 76 may include one or more detection channels, each ofwhich may comprise an amplifier. The detection channels may be used tosense the cardiac signals. Some detection channels may detect events,such as R-waves or P-waves, and provide indications of the occurrencesof such events to processor 70. One or more other detection channels mayprovide the signals to an analog-to-digital converter, for conversioninto a digital signal for processing or analysis by processor 70 orepisode classifier 80.

For example, sensing module 76 may comprise one or more narrow bandchannels, each of which may include a narrow band filteredsense-amplifier that compares the detected signal to a threshold. If thefiltered and amplified signal is greater than the threshold, the narrowband channel indicates that a certain electrical cardiac event, e.g.,depolarization, has occurred. Processor 70 then uses that detection inmeasuring frequencies of the sensed events.

In one example, at least one narrow band channel may include an R-waveor P-wave amplifier. In some examples, the R-wave and P-wave amplifiersmay take the form of an automatic gain controlled amplifier thatprovides an adjustable sensing threshold as a function of the measuredR-wave or P-wave amplitude. Examples of R-wave and P-wave amplifiers aredescribed in U.S. Pat. No. 5,117,824 to Keimel et al., which issued onJun. 2, 1992 and is entitled, “APPARATUS FOR MONITORING ELECTRICALPHYSIOLOGIC SIGNALS,” and is incorporated herein by reference in itsentirety.

In some examples, sensing module 76 includes a wide band channel whichmay comprise an amplifier with a relatively wider pass band than thenarrow band channels. Signals from the electrodes that are selected forcoupling to the wide-band amplifier may be converted to multi-bitdigital signals by an analog-to-digital converter (ADC) provided by, forexample, sensing module 76 or processor 70. Processor 70 may analyze thedigitized version of signals from the wide band channel. Processor 70may employ digital signal analysis techniques to characterize thedigitized signals from the wide band channel to, for example, detect andclassify the patient's heart rhythm.

Episode classifier 80 may detect and classify the patient's heart rhythmbased on the cardiac electrical signals sensed by sensing module 76employing any of the numerous signal processing methodologies known inthe art. For example, episode classifier 80 may maintain escape intervalcounters that may be reset upon sensing of R-waves by sensing module 76.The value of the count present in the escape interval counters whenreset by sensed depolarizations may be used by episode classifier 80 tomeasure the durations of R-R intervals, which are measurements that maybe stored in memory 72. Episode classifier 80 may use the count in theinterval counters to detect a tachyarrhythmia, such as ventricularfibrillation or ventricular tachycardia. A portion of memory 72 may beconfigured as a plurality of recirculating buffers, capable of holdingseries of measured intervals, which may be analyzed by episodeclassifier 80 to determine whether the patient's heart 12 is presentlyexhibiting atrial or ventricular tachyarrhythmia.

In some examples, episode classifier 80 may determine thattachyarrhythmia has occurred by identification of shortened R-R intervallengths. Generally, episode classifier 80 detects tachycardia when theinterval length falls below 360 milliseconds (ms) and fibrillation whenthe interval length falls below 320 ms. These interval lengths aremerely examples, and a user may define the interval lengths as desired,which may then be stored within memory 72. This interval length may needto be detected for a certain number of consecutive cycles, for a certainpercentage of cycles within a running window, or a running average for acertain number of cardiac cycles, as examples.

In some examples, an arrhythmia detection method may include anysuitable tachyarrhythmia detection algorithms. In one example, episodeclassifier 80 may utilize all or a subset of the rule-based detectionmethods described in U.S. Pat. No. 5,545,186 to Olson et al., entitled,“PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENTOF ARRHYTHMIAS,” which issued on Aug. 13, 1996, or in U.S. Pat. No.5,755,736 to Gillberg et al., entitled, “PRIORITIZED RULE BASED METHODAND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” which issuedon May 26, 1998. U.S. Pat. No. 5,545,186 to Olson et al. and U.S. Pat.No. 5,755,736 to Gillberg et al. are incorporated herein by reference intheir entireties. However, other arrhythmia detection methodologies mayalso be employed by episode classifier 80 in some examples. For example,EGM morphology may be considered in addition to or instead of intervallength for detecting tachyarrhythmias.

Generally, episode classifier 80 detects a treatable tachyarrhythmia,such as VF, based on the EGM, e.g., the R-R intervals and/or morphologyof the EGM, and selects a therapy to deliver to terminate thetachyarrhythmia, such as a defibrillation pulse of a specifiedmagnitude. The detection of the tachyarrhythmia may include a number ofphases or steps prior to delivery of the therapy, such as first phase,sometimes referred to as detection, in which a number of consecutive orproximate R-R intervals satisfies a first number of intervals to detect(NID) criterion, a second phase, sometimes referred to as confirmation,in which a number of consecutive or proximate R-R intervals satisfies asecond, more restrictive NID criterion. Tachyarrhythmia detection mayalso include confirmation based on EGM morphology or other sensorssubsequent to or during the second phase. Again, in some cases, episodeclassifier 80 may mistakenly classify the patient's heart rhythm as atreatable tachyarrhythmia, e.g., as a result of a noisy EGM orover-sensing. In order to learn more about when IMD 16 is misclassifyingpatient's heart rhythms as shockable episodes, episode classifier 80 maysend a portion of an EGM signal that resulted in a classification of atreatable tachyarrhythmia.

In some examples, episode classifier 80 sends a portion of the EGMsignal to memory 72 to be saved on an ongoing basis. When atachyarrhythmia is not detected the EGM signal may be written over aftera period of time. In response to a tachyarrhythmia being detected,episode classifier 80 may direct memory 72 to store on a long term basisa time period or portion of the EGM signal leading up to the diagnosisof the tachyarrhythmia, along with the specific diagnosis, e.g.,ventricular tachycardia, ventricular fibrillation or supraventriculartachycardia. In some examples, a diagnosis may not result in stimulationbeing provided by IMD 16. The corresponding EGM signal may becategorized as non-sustained ventricular tachycardia atrial tachycardiaor atrial fibrillation or a monitored ventricular tachycardia episode.

Episode classifier 80 or processor 70 may implement one or morealgorithms to determine if VOS, TWOS, or atrial sensing issues arepresent. The presence of one or more of VOS, TWOS, or atrial sensing mayaffect the episode classification by episode classifier 80, as well aspossible treatment selection by processor 70.

Although processor 70 and episode classifier 80 are illustrated asseparate modules in FIG. 3, processor 70 and episode classifier 80 maybe incorporated in a single processing unit. Episode classifier 80 maybe a component of, or a software or firmware module executed by,processor 70.

Activity sensor 82 may be optionally included in some examples of IMD16. Activity sensor 82 may include one or more accelerometers. Activitysensor 82 may additionally or alternatively include other sensors suchas a heart sounds sensor, a pressure sensor, or an O₂ saturation sensor.In some examples, activity sensor 82 may detect respiration via one ormore electrodes. Information obtained from activity sensor 82 may beused to determine activity level, posture, blood oxygen level orrespiratory rate, for example, leading up to, or at the time of, theabnormal heart rhythm. In some examples, this information may be used byIMD 16 to aid in the classification of an abnormal heart rhythm.

Activity sensor 82 may, for example, take the form of one or moreaccelerometers, or any other sensor known in the art for detectingactivity, e.g., body movements or footfalls, or posture. In someexamples, activity sensor 82 may comprise a three-axis accelerometer.Processor 70 may determine an activity level count at regular intervalsbased on the signal(s) from activity sensor 82. In some examples,processor 70 may determine a running average activity count based on theinformation provided by activity sensor 82. For example, the activitycount may be calculated over a 1 second interval and the processor 70may update the activity level count at a 1 second interval. A method ofdetermining activity count from an accelerometer sensor is described inU.S. Pat. No. 6,449,508, to Sheldon et al, entitled, “ACCELEROMETERCOUNT CALCULATION FOR ACTIVITY SIGNAL FOR AN IMPLANTABLE MEDICALDEVICE,” issued Sep. 10, 2002, and incorporated herein by reference inits entirety.

Activity sensor 82 may be located outside of the housing 8 of IMD 16.Activity sensor 82 may be located on a lead that is coupled to IMD 16 ormay be implemented in a remote sensor that wirelessly communicates withIMD 16 via telemetry module 78. In any case, activity sensor 82 iselectrically or wirelessly coupled to circuitry contained within housing8 of IMD 16.

Telemetry module 78 includes any suitable hardware, firmware, softwareor any combination thereof for communicating with another device, suchas programmer 24 (FIG. 1). Under the control of processor 70, telemetrymodule 78 may receive downlink telemetry from and send uplink telemetryto programmer 24 with the aid of an antenna, which may be internaland/or external. In some examples, processor 70 may transmit cardiacsignals, e.g., ECG or EGM signals, produced by sensing module 76 and/orsignals selected by episode classifier 80 to programmer 24. Processor 70may also generate and store marker codes indicative of different cardiacor other physiological events detected by sensing module 76 or episodeclassifier 80, and transmit the marker codes to programmer 24. Anexample IMD with marker-channel capability is described in U.S. Pat. No.4,374,382 to Markowitz, entitled, “MARKER CHANNEL TELEMETRY SYSTEM FOR AMEDICAL DEVICE,” which issued on Feb. 15, 1983 and is incorporatedherein by reference in its entirety. Information which processor 70 maytransmit to programmer 24 via telemetry module 78 may also include anindication of a change in disease state of the heart, an indication of achange in heart response to the therapy provided or an indication thatthe heart continues to response in the same (or similar) manner to thetherapy provided, the indications based on heart sounds and/or EGMsignals. Such information may be included as part of a marker channelwith an EGM.

FIG. 4 is a block diagram illustrating an example external programmer24. As illustrated in FIG. 4, programmer 24 may include a processor 84,a memory 92, a telemetry module 86, a user interface 88, a power source90 and an arrhythmia analyzer 98. Processor 84 stores and retrievesinformation and instructions to and from memory 92. Processor 84 mayinclude a microprocessor, a microcontroller, a DSP, an ASIC, an FPGA, orother equivalent discrete or integrated logic circuitry. Accordingly,processor 84 may include any suitable structure, whether in hardware,software, firmware or any combination thereof, to perform the functionsascribed herein to processor 84.

Telemetry module 86 receives EGM signal data from IMD 16. In someexamples, the EGM Signal data is transmitted from IMD via access point106 and network 100, as shown in FIG. 5. The EGM signal data may betransmitted to telemetry module 86 in response to IMD 16 diagnosing anarrhythmia and responding with electrical stimulation. In some examples,portions of EGM signal data are stored in memory 72 of IMD 16 until apredetermined event occurs. After the event has occurred, the data istransmitted via telemetry module 78 of IMD 16 to telemetry module 86 ofprogrammer 24. For example, every three months IMD 16 may transmit EGMsignal data selected by episode classifier 80 and stored in memory 72.

A user, such as a clinician or patient, may interact with programmer 24through user interface 88. Accordingly, in some examples programmer 24may comprise a patient programmer or a clinician programmer. Thetechniques of this disclosure are directed post-processing of EGMsignals collected by IMD 16 and used by IMD 16 to diagnosis treatablearrhythmias. The post-processing is used to determine whether IMD 16correctly diagnosed the detected arrhythmia. Therefore, many of thefunctions ascribed to programmer 24, and in particular processor 84, maybe performed by any one or more external devices, such as any one ormore of programmer 24, external device 104 (FIG. 5) or another computingdevice, e.g., computing device 108 (FIG. 5). In some examples programmer24 may function as a user interface while processing occurs on externaldevice 104. When programmer 24 is configured as a patient programmer, insome examples, the patient programmer is not necessarily configured toperform the post-processing or provide information regarding theaccuracy of diagnosis to the patient. In some examples, when programmer24 is configured as a clinician programmer, processor 84 may beconfigured to perform the post-processing using arrhythmia analyzer 98and arrhythmia analyzer rules 96.

Although processor 84 and arrhythmia analyzer 98 are illustrated asseparate modules in FIG. 4, processor 84 and arrhythmia analyzer 98 maybe incorporated in a single processing unit. Arrhythmia analyzer 98 maybe a component of or a module executed by processor 84.

User interface 88 includes a display (not shown), such as a LCD or LEDdisplay or other type of screen, to present information related to thetherapy, such as information related to current stimulation parametersand electrode combinations and when configured to allow a physician toreview EGM information transmitted from IMD 16, including informationregarding cardiac episode classification by episode classifier 80 of IMD16. In some examples, user interface 88 may display informationregarding the results of arrhythmia analyzer 98. In addition, userinterface 88 may include an input mechanism to receive input from theuser. The input mechanisms may include, for example, buttons, a keypad(e.g., an alphanumeric keypad), a peripheral pointing device, or anotherinput mechanism that allows the user to navigate through user interfacespresented by processor 84 of programmer 24 and provide input. The inputmay include, for example, selection of one or more cardiac episodestransmitted from IMD 16 for arrhythmia analysis by arrhythmia analyzer98.

If programmer 24 includes buttons and a keypad, the buttons may bededicated to performing a certain function, e.g., a power button, or thebuttons and the keypad may be soft keys that change in functiondepending upon the section of the user interface currently viewed by theuser. Alternatively, the display (not shown) of programmer 24 may be atouch screen that allows the user to provide input directly to the userinterface shown on the display. The user may use a stylus or a finger toprovide input to the display. In other examples, user interface 88 alsoincludes audio circuitry for providing audible instructions or sounds topatient 14 and/or receiving voice commands from patient 14, which may beuseful if patient 14 has limited motor functions.

Patient 14, a clinician, or another user may also interact withprogrammer 24 to manually select values for operational parameters ofIMD 16, and thereby control the cardiac sensing and stimulationfunctionality of the IMD. In some examples, modification to operationalparameters may be made in response to the results of arrhythmia analysisby arrhythmia analyzer 98. For example, programmer 24 may modifydetection algorithms used by episode classifier 80 in response to theresults of arrhythmia analysis of one more episodes by arrhythmiaanalyzer 98.

Processor 84 receives a segment of EGM signal data representing acardiac episode resulting in a diagnosis of an arrhythmia followed byelectrical stimulation based on the diagnosis. The episode may bereceived from telemetry module 86 or from memory 92. The episodesreceived from IMD 16 may be stored in stored episodes 94 until retrievedby processor 84 or arrhythmia analyzer 98 for display or classification.Arrhythmia analyzer 98 may use arrhythmia analyzer rules stored inarrhythmia analyzer rules 96 to analyze a cardiac episode. Processor 84may select stored episodes 94 for retrospective analysis based onwhether the diagnosis of the cardiac episode by episode classifier 80 ofIMD 16 and the classification by arrhythmia analyzer 98 conflict.

As shown in FIG. 4, memory 92 includes stored episodes 94, andarrhythmia analyzer rules 96 in separate memories within memory 92 orseparate areas within memory 92. Memory 92 may also include instructionsfor operating user interface 88, telemetry module 86, and for managingpower source 90. Memory 92 may include any volatile or nonvolatilememory such as RAM, ROM, EEPROM or flash memory. Memory 92 may alsoinclude a removable memory portion that may be used to provide memoryupdates or increases in memory capacities. A removable memory may alsoallow sensitive patient data to be removed before programmer 24 is usedby, or for, a different patient.

Stored episodes 94 stores EGM signal data received from IMD 16 viatelemetry module 86. In some examples, the EGM signal data is separatedinto episodes, and each episode is saved along with a diagnosis made byIMD 16 based on the EGM signal data in the episode. IMD 16 may transmitEGM signal data at predetermined time intervals, for example every threemonths. The EGM signals are received by telemetry module 86 and storedin stored episodes 94. In some examples, processor 84 retrieves episodesstored in stored episodes 94 one at a time and confirms or rejects thediagnosis of IMD 16 using arrhythmia analyzer rules stored in episodeclassification rule 96.

Arrhythmia analyzer rules 96 stores one or more classificationalgorithms or sets of classification rules used by arrhythmia analyzer98 to perform retrospective arrhythmia analysis to classify cardiacepisodes transmitted by IMD 16 to programmer 24. In some examples, thearrhythmia analyzer rules classify each episode as supraventriculartachycardia (SVT), ventricular tachycardia or ventricular fibrillation(VT/VF), or unknown. The arrhythmia analyzer rules may also determine ifany misclassifications are based on VOS or TWOS. The classificationrules may, in some examples, provide comments regarding reason for aparticular classification, including, for example, whether VOS or TWOSwas present. In some examples, the classifications are compared to thediagnosis generated by IMD 16 prior to delivery therapy.

Arrhythmia analyzer 98 may apply arrhythmia analyzer rules stored inarrhythmia analyzer rules 96 to a cardiac episode. Episodes receivedfrom IMD 16 may be stored in stored episodes 94 until retrieved byarrhythmia analyzer 98 for classification. In addition to aclassification, arrhythmia analyzer 98 may also determine whether theEGM signal of the cardiac episode indicates the presence of one orsensing problems such as VOS and TWOS.

FIG. 5 is a block diagram illustrating an example system that includesan external device 104, such as a server, and one or more computingdevices 108A-108N that are coupled to the IMD 16 and programmer 24 shownin FIG. 1 via a network 100. Network 100 may be generally used totransmit diagnostic information (e.g., a diagnosis made by IMD 16 of anabnormal cardiac rhythm based on an EGM signal obtained by the IMD) froman IMD 16 to a remote external computing device. In some examples, EGMsignals may be transmitted to an external device for display to a user.In some examples, the EGM signal is subjected to retrospective analysisby the external device resulting in a post-processing classification ofthe cardiac episode.

In some examples, the information transmitted by IMD 16 may allow aclinician or other healthcare professional to monitor patient 14remotely. In some examples, IMD 16 may use a telemetry module 78 tocommunicate with programmer 24 via a first wireless connection, and tocommunicate with access point 106 via a second wireless connection,e.g., at different times. In the example of FIG. 5, access point 106,programmer 24, server 104 and computing devices 108A-108N areinterconnected, and able to communicate with each other through network100. In some cases, one or more of access point 106, programmer 24,server 104 and computing devices 108A-108N may be coupled to network 100via one or more wireless connections. IMD 16, programmer 24, server 104,and computing devices 108A-108N may each comprise one or moreprocessors, such as one or more microprocessors, DSPs, ASICs, FPGAs,programmable logic circuitry, or the like, that may perform variousfunctions and operations, such as those described herein.

Access point 106 may comprise a device that connects to network 100 viaany of a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem connections. In other examples,access point 106 may be coupled to network 100 through different formsof connections, including wired or wireless connections. In someexamples, access point 106 may be co-located with patient 14 and maycomprise one or more programming units and/or computing devices (e.g.,one or more monitoring units) that may perform various functions andoperations described herein. For example, access point 106 may include ahome-monitoring unit that is co-located with patient 14 and that maymonitor the activity of IMD 16. In some examples, server 104 orcomputing devices 108 may control or perform any of the variousfunctions or operations described herein, e.g., determine, based on EGMsignal data, whether IMD 16 properly classified various cardiacepisodes, and display a summary of the EGM signal data transmitted byIMD 16.

In some cases, server 104 may be configured to provide a secure storagesite for archival of diagnostic information (e.g., occurrence of adiagnosis and shock by IMD 16 and attendant circumstances such as theEGM signal leading up to the diagnosis) that has been collected andgenerated from IMD 16 and/or programmer 24. Network 100 may comprise alocal area network, wide area network, or global network, such as theInternet. In some cases, programmer 24 or server 104 may assemble EGMsignal and diagnosis information in web pages or other documents forviewing by trained professionals, such as clinicians, via viewingterminals associated with computing devices 108. The system of FIG. 5may be implemented, in some aspects, with general network technology andfunctionality similar to that provide by the Medtronic CareLink® Networkdeveloped by Medtronic, Inc., of Minneapolis, Minn.

In the example of FIG. 5, external server 104 may receive EGM signaldata from IMD 16 via network 100. Based on the EGM signal data received,processor(s) 102 may preform one or more of the functions described withherein with respect to processor 84 and/or arrhythmia analyzer 98 ofprogrammer 24, e.g., processor(s) 102 of server 104 may implement orcomprise an arrhythmia analyzer 98 that analyzes EGM signals from IMD 16according to arrhythmia analyzer rules 96. Computing device 108 may alsoinclude a processor that performs one or more of the functions describedherein with respect to processor 84 and/or arrhythmia analyzer 98 ofprogrammer 24. In various examples, arrhythmia analysis may be carriedout by any of the programmer 24, external server 104 or computing device108.

FIG. 6 is a flow chart illustrating an example arrhythmia analysissequence implemented by arrhythmia analyzer 98, which may be implementedin any one or more programmer 24, external server 104, computing device108, any other computing device, or any combination thereof. Arrhythmiaanalyzer 98 retrieves a shocked episode (110) from stored episodes 94.The arrhythmia analyzer 98 determines whether the EGM signal of theepisode indicates the presence of the VOS (112). As explained in moredetail below with respect to FIG. 7, arrhythmia analyzer 98 makes aprobabilistic determination of whether VOS is present based on a numberof criteria. Each criteria is assigned a weight for or against thepresence of VOS, and based on the net outcome of the evaluation,arrhythmia analyzer 98 determines whether it is likely VOS is present inthe EGM signal for the cardiac episode. If arrhythmia analyzer 98determines that VOS is present, the arrhythmia analyzer 98 classifiesthe cardiac episode as one with receiving an inappropriate shock (114).

If VOS is not present, then arrhythmia analyzer 98 determines whetherthe EGM signal for the cardiac episode indicates the presence of atrialsensing issues (116). Although FIG. 6 illustrates determining VOS asoccurring prior to determining whether the cardiac episodes includesatrial sensing issues, in other examples, not illustrated, the atrialsensing issue determination may be made prior to a determination of thepresence of VOS. If arrhythmia analyzer 98 determines the presence ofatrial sensing issues, arrhythmia analyzer 98 then determines if theatrial sensing issues are repairable (118). If the atrial sensing issuesare not repairable, arrhythmia analyzer 98 applies logic (120) that doesnot rely on good atrial sensing. Factors that may be used to classify acardiac episode in the presence of atrial sensing issues include, forexamples, RR interval regularity or rate, the presence of atrialfibrillation (AF) characteristics, the rhythm after pacing, thefrequency of the V signal, and ventricular morphology rules. Based onthe classification rules, arrhythmia analyzer 98 may classify thecardiac episode as VT/VF, inappropriate (or SVT), or indeterminate(122). These factors will be described in more detail below with respectto FIG. 10.

A classification of VT/VF indicates that the arrhythmia analyzer 98agrees with the classification by episode classifier 80 of IMD 16, andthe decision to shock based on the EGM signal associated with thecardiac episode. A classification of inappropriate indicates that thearrhythmia analyzer 98 classified the cardiac episode as SVT, andtherefore the shock provided was inappropriate treatment for the cardiacepisode. A classification of indeterminate indicates that arrhythmiaanalyzer 98 was unable to determine whether the cardiac episode wasproperly classified as VT/VF or not.

If no atrial sensing issues are present, arrhythmia analyzer 98continues to perform episode classification using algorithms that relyon one or both of atrial sensed events and ventricular sensed events. Ifthe atrial sensing issues are repairable, then arrhythmia analyzer 98 orprocessor 84 repair the atrial sensing issues within the cardiacepisode. After the atrial sensing issues are repaired, arrhythmiaanalyzer 98 continues to perform episode classification using algorithmsthat relay on one or both of atrial sensed events and ventricular sensedevents. To that end, arrhythmia analyzer 98 may determine the ratio ofatrial sensed events to ventricular sensed events (A/V ratio) (124).

If the A/V ratio (124) indicates there are less atrial sensed eventsthan ventricular sensed events (126), then arrhythmia analyzer 98classifies the cardiac episode as VT/VF. In the event that the number ofatrial sensed events approximately equal the number of ventricularevents (A=V) (130), the arrhythmia analyzer 98 applies logic (132)specific to cardiac episodes with an A=V ratio in order to classify thecardiac episode as VT/VF, inappropriate, or indeterminate (134). Asdiscussed above, a classification by arrhythmia analyzer 98 as VT/VFconfirms the episode classification by episode classifier 80 of IMD 16,a classification by arrhythmia analyzer 98 as inappropriate indicatesarrhythmia analyzer 98 determined the cardiac episode was SVT and thatIMD 16 inappropriately classified and treated the cardiac episode with ashock, and a classification as indeterminate indicates that arrhythmiaanalyzer 98 was unable to conclusively determine whether the cardiacepisode was VT/VF or SVT.

If the A/V ratio (124) indicates that the number of atrial sensed eventsis greater than the number of ventricular sensed events (A>V)(136), thenarrhythmia analyzer 98 applies logic (138) specific to cardiac episodeswith an A>V ratio in order to classify the cardiac episode as VT/VF,inappropriate, or indeterminate (140). As discussed above, aclassification by arrhythmia analyzer 98 as VT/VF confirms the episodeclassification by episode classifier 80 of IMD 16, a classification byarrhythmia analyzer 98 as inappropriate indicates arrhythmia analyzer 98determined the cardiac episode was SVT and that IMD 16 inappropriatelyclassified and treated the cardiac episode with a shock, and aclassification as indeterminate indicates that arrhythmia analyzer 98was unable to conclusively determine whether the cardiac episode wasVT/VF or SVT.

FIG. 7 is a flow chart illustrating an example method of determining thepresence of VOS using a probabilistic analysis. Although discussed withrespect to implementation by arrhythmia analyzer 98, a probabilistic VOSalgorithm such as the one discussed with respect to FIG. 7 may beimplemented in real time by episode classifier 80 of IMD 16. In additionarrhythmia analyzer 98 may be located in any of a number of externaldevices. For example, the method may be implemented by external server104, computing device 108, or programmer 24.

In some examples, arrhythmia analyzer 98 applies a plurality of weightedcriteria a cardiac episode (146). The criteria may include considerationof the number of R-R intervals of certain lengths. For example, thenumber of R-R intervals with lengths less than 130 milliseconds (ms),the number of intervals with a length between 131 ms and 160 ms, thenumber of intervals with a length between 271 ms and 349 ms, and thenumber of intervals with a length greater than or equal to 350 ms.

The criteria may also include the regularity of ventricular intervalsduring the episode. In some examples, the episode may be classified asirregular, regular, or very regular, depending on the regularity of theventricular intervals during the episode.

The regularity may be determined based on the consistency of intervallengths. For example, the regularity may be determined based on thecumulative differences between consecutive intervals. More particularly,the sum of the absolute values of the differences between theconsecutive intervals prior to detection may be compared to one or morethresholds to classify the episode as regular, irregular, or veryregular. In one example, the sum must be less than or equal to a firstthreshold to be classified as regular, and less than or equal to asecond, lower threshold to be classified as very regular. The sum of theabsolute values of the differences may be called a factor. For example,a factor of 6 would indicate that the sum of the difference for theintervals examined is 6 ms. In addition, in some examples, eachconsecutive change in interval length may not be greater than athreshold, such as 40 ms, for the episode to be considered regular.

To be considered extremely regular, 10 consecutive intervals areexamined and the threshold to be considered extremely regular is afactor of 6. That is, for at least 10 consecutive intervals the sum ofthe absolute values of the differences between the consecutive intervalsis less than 6 ms. For very regular episodes, 12 consecutive intervalsare used and a factor of 14 is used as the threshold. For regularepisodes, 10 consecutive intervals are used along with a factor of 25for the threshold.

Arrhythmia analyzer 98 may additionally or alternatively determinewhether an episode included a regular rhythm based on a comparison of aninterval of an episode to the previous two intervals of the episode. Insome examples, the determination of regularity may be made based in parton the equation:min(|(i−1)|/I,[(i−2)−i/I,|[(i−1)+(i−2)]−i|/i,|[|(i−1)−(i−2)|]−i|/i)Wherein, i equals the current interval, i−1 equals the previousinterval, and i−2 equals the interval prior to i−1. A rhythm isconsidered regular if a preset number of the RR intervals just prior todetection of the arrhythmia have a value from the equation that fallsbelow a preset threshold. In some examples, 7 out of 12 of the RRintervals must have a result from the equation of less than thethreshold, e.g., less than approximately 0.12 or less.

The criteria may also include various criteria related to whether theEGM may have been influenced by electromagnetic interference or othernoise sources. For example, the criteria may include the noise level ofthe EGM signal, whether there are bursts of noise in the EGM signal, orwhether there is evidence of EGM saturation. The criteria for andagainst VOS may also include whether these is a sinusoidal patternwithin the EGM, or whether the RR interval distribution is typical ofVF. The criteria may also include whether baseline periods are presentin the EGM signal of the cardiac episode, whether a far-field (FF) EGMsignal associated with the cardiac episodes includes evidence ofover-sensing in the FF, the signal frequency content of the EGM signal,evidence of electromagnetic interference (EMI), or evidence ofmyopotentials. A baseline period is a period of a flat EGM signal. Forexample, the EGM signal includes, no activity and no noise sensed. Thecriteria may also include evidence of TWOS, which is discussed in moredetail below with respect to FIG. 8. In some examples, the criteria mayinclude evidence of R-wave over-sensing (RWOS), whether there is apattern of RR (or VV) interval rate changes, and/or whether there isvariation in slew within the episode. In some examples, the slew of abeat within the cardiac episode is the slope of the R-wave.

After arrhythmia analyzer 98 has analyzed the EGM signal for the cardiacepisode for each of the criteria, the arrhythmia analyzer 98 determinesthe amount of evidence for and against VOS (148). Below is a list ofpossible uses of the criteria above with example weights used. The listand weights are not intended to be limiting. In some examples, if thereare two or more intervals less than 160 ms in length, then a +1 is addedto the evidence for VOS. If there are eleven or more intervals between161 ms and 270 ms in length, then +1 is added to evidence of non-VOS. Ifthere are more than six intervals with a length between 271 ms and 349ms then +1 is added to the evidence of non-VOS. If there are more thanseven intervals with a length of 350 ms or greater, then +1 is added toevidence of non-VOS. In some examples, the level of noisiness of the EGMsignal is used as a factor for or against the presence of VOS. Ifarrhythmia analyzer 98 determines the EGM signal to be noisy, then +1 isadded to VOS evidence. If arrhythmia analyzer 98 determines the EGMsignal is very noisy or extremely noisy, then +2 is added to VOSevidence, if arrhythmia analyzer 98 determines there is EGM signalsaturation, then +3 is added to VOS evidence, and if arrhythmia analyzer98 determines the EGM signal is not noise, then +1 is added to non-VOSevidence. In some examples arrhythmia analyzer 98 may determine that aVOS pattern is present on the FF EGM signal. The presence of a VOSpattern on the FF EGM signal is a +3 for VOS evidence. If arrhythmiaanalyzer 98 determines that EMI is present then +3 is added to VOSevidence. If arrhythmia analyzer 98 determines that myopotenials arepresent in the EGM signal, then +3 is added to VOS evidence. In someexamples, arrhythmia analyzer 98 analyzes the sinusoidal patter of theEGM signal on the FF EGM channel. If the slew is consistent for V beatson the FF EGM channel, then +1 for non-VOS evidence. If the slew is notconsistent for V beats on the FF EGM channel, then +1 for VOS evidence.In some examples, arrhythmia analyzer 98 examines the RR distribution.If the RR distribution is not typical of VF, then +1 for VOS evidence.

Arrhythmia analyzer 98 may implement an algorithm to determine whetherthe EGM signal of the cardiac episode indicates TWOS. An examplealgorithm for detecting TWOS is described below with respect to FIG. 8.If arrhythmia analyzer 98 determines that TWOS is present, then +5 forVOS evidence. Arrhythmia analyzer 98 may also examine the EGM signal forthe presence of RWOS. If RWOS is present, then +3 for VOS evidence.

Some factors may be evaluated in combination to determine whether to addweight to VOS evidence or to non-VOS evidence. For example, acombination of a number of intervals less than 130 ms and an irregularrhythm results in a +1 for evidence of VOS. The combination of a regularepisode, a regular rhythm and no intervals under 130 ms results in a +1for non-VOS. The combination of a low frequency EGM signal content, noEGM saturation and no evidence of TWOS results in a +4 for non-VOS. Thecombination of a sudden onset of fast VV rate that remains fast, noevidence of TWOS, and a VOS pattern not found on the FF EGM results in a+3 added to non-VOS evidence.

After arrhythmia analyzer 98 has applied preselected weighted criteriato the cardiac episode, arrhythmia analyzer 98 determines the totalamount of evidence for and against VOS (148). This may be done by addingup the weighted factors indicating VOS and the weighted factorsindicating no VOS separately. In some examples, the evidence of VOS maybe given a positive weight while the weighted factors indicating no VOSmay be subtracted from the total weight for VOS. For example, instead to+1 for non-VOS evidence as described above, 1 would be subtracted fromVOS evidence for a criterion with a 1 weight being met for non-VOS.Arrhythmia analyzer 98 determines whether the VOS evidence is less thanthe non-VOS evidence (150), whether the VOS evidence is approximatelyequal to the non-VOS evidence (152), whether the VOS-evidence is greaterthan the non-VOS evidence (154) or whether the VOS evidence is muchgreater than the non-VOS evidence (156). If the VOS evidence is lessthan or approximately equal to the non VOS evidence, then arrhythmiaanalyzer 98 determines there was no VOS (158) present in the cardiacepisode and proceeds to use one or more algorithms to classify thecardiac episode (164). If there is more evidence of VOS than evidenceagainst VOS, arrhythmia analyzer 98 determines that the cardiac episodemay include VOS (160). If there is much more evidence of VOS then of noVOS then arrhythmia analyzer 98 determines that the cardiac signallikely includes VOS (162).

FIG. 8 is a flow diagram illustrating an example method of determiningwhether T-TWOS is present in an EGM signal detected by IMD 16. In someexamples, an algorithm for detecting TWOS may be used by episodeclassifier 80 during real time examination of an EGM signal fordiagnosis of abnormal cardiac episodes. In some examples, arrhythmiaanalyzer 98 determines whether TWOS is present in a stored cardiacepisode. The example illustrated in FIG. 8 is one in which arrhythmiaanalyzer 98 determines whether TWOS is present in a cardiac episode.However, it will be appreciated that episode classifier 80 may similarlyperform the example method of FIG. 8.

The arrhythmia analyzer 98 receives an EGM signal (172) from memory 92for analysis. The arrhythmia analyzer 98 searches the cardiac episodefor the presence of runs of consecutive R-waves and/or R-R intervalswith alternating characteristics (174). A run of alternatingcharacteristic is a number of beats in a row within the cardiac episodein which a given beat within the has different characteristics than thebeat immediately preceding the beat and beat immediately after the beat,and similar characteristics to the beat two prior to the beat and thebeat two after the beat within the run. The alternating characteristicsmay be, for example, alternating R-wave morphology, slew rate, oramplitude. In some examples, arrhythmia analyzer 98 may look at a numberof beats, e.g., 24, proceeding detection of an arrhythmia by cardiacepisode classifier 80. The arrhythmia analyzer 98 may look for multipledifferent alternating runs within the cardiac episode. The arrhythmiaanalyzer 98 returns the longest runs starting from each starting beatposition, e.g., starting positions 1 through 24. In some examples, runsmay overlap. For example, a particular beat may be the starting beat fora run of alternating slew rates as well as a run of alternatingamplitudes. Arrhythmia analyzer 98 returns the run which is the longestbetween the different runs starting from the same beat. Any shorter runsmay be removed as redundant. In some examples, redundant runs may alsobe removed if there is almost complete overlap. For example, if a firstrun starts at beat 1 and lasts for 6 beats and a second run starts atbeat 2 and last for 8 beats, the first run may be removed as redundantas there the is only one beat that is not overlapping.

Arrhythmia analyzer 98 also clusters RR intervals within the cardiacepisode based on interval length (176). Arrhythmia analyzer 98determines the length of each interval between each R-wave within thecardiac episode. Arrhythmia analyzer 98 then clusters, or sorts, theintervals into groups where the intervals within the group are close invalue and where there is a distinct separation from values in otherclusters.

For example, interval lengths between 180 ms and 210 ms may make up onecluster, while interval lengths between 240 ms and 270 ms are withinanother cluster. There may be no or very few intervals with lengthsbetween 210 and 240 ms.

Arrhythmia analyzer 98 may cluster intervals by placing each intervalvalue in an array of bins e.g., each bin including an X ms range, forexample, and sorting the array. Arrhythmia analyzer 98 may then countthe number of interval values in each bin, and then looks for bins orconsecutive bins with no intervals, or only one interval. In someexamples, a stretch of interval length value bins with no intervals oronly one interval is considered a “dead zone,” or an area betweenclusters. The dead zone may be between 5 and 25 ms in length. In someexamples, the length of the stretch is programmable by a clinician orother user. In some examples, a default dead zone length may beapproximately 10 ms.

Arrhythmia analyzer 98 then examines the possible clusters between thedead zones. In some examples, arrhythmia analyzer 98 may look for theaverage interval length value of the intervals within the cluster andthe distribution around the average of the intervals within theclusters. The possible clusters may be broken up into additionalclusters based on such a second sorting. In some examples, clusters withhigher interval values may include a wider range of interval values thanclusters with lower interval length values.

After clustering of interval values, arrhythmia analyzer 98 may examinethe cardiac episodes in two ways. Arrhythmia analyzer 98 searches thealternating characteristic runs for alternating intervals (178). Forexample, arrhythmia analyzer 98 may determine whether the intervalswithin a run alternate with respect to into which cluster the intervalshave been grouped. In some examples, arrhythmia analyzer 98 may considera run to include alternating intervals if the run includes at least 3alternating intervals. In some examples, the entire run examinedincludes alternating intervals. Arrhythmia analyzer 98 may provide alist of each of the runs with alternating intervals. Alternatively, insome examples, arrhythmia analyzer 98 may provide the longest run withalternating intervals. In some examples, arrhythmia analyzer 98 may keepa count of the total number of alternating intervals over the entirecardiac episode.

Arrhythmia analyzer 98 may also examine the alternating characteristicruns for alternating beat, e.g., R-wave widths (178). In some examples,arrhythmia analyzer 98 determines that there are not alternating widthsif, for any of the beats, the difference between the current andprevious width is less than 20% of the current width, or the differencebetween the current width and the second previous width is greater than20%. In some examples, a count is keep of the number of alternatingwidths as each beat is examined, to determine the length of the run ofalternative widths. In some examples, if the widths interval lengthsremained alternating within the run for at least 2 beats, then the runis considered to include alternating widths. In some examples acumulative count of the alternating widths is kept for each run.

Arrhythmia analyzer 98 may also examine interval clusters for TWOScharacteristics (182). Arrhythmia analyzer 98 first identifies whichcluster(s) have short intervals and which cluster has longer intervals.If there are two clusters, then one is labeled short and the other islabeled long. If there are three clusters, than one is labeled long andthe other two are labeled short. Arrhythmia analyzer 98 determines ifeach cluster has more than two intervals. Arrhythmia analyzer 98 thendetermines if the shorter cluster(s) sum to equal the third. If thereare two clusters, arrhythmia analyzer 98 determines the short clustersums to the longer cluster if double the mean for the short cluster isclose to the mean of the long cluster. In some examples, the sum must bewithin plus or minus a predetermined percentage of the longer cluster'smean interval length. In some examples the percentage may beapproximately 6%.

If there are three clusters, then the means of the first short clusterand the second short cluster are summed. If the sum is close to the meanof the long cluster, the shorter clusters are considered to sum to thelonger cluster. In some examples the sum is considered to be close ifthe sum is within plus or minus a predetermined percentage of the longercluster's mean interval length. In some examples, the percentage may beapproximately 6%. If the short cluster(s) sum to the longer cluster,arrhythmia analyzer 98 determines that the cardiac episode is displayingTWOS characteristics.

Arrhythmia analyzer 98 may check additional characteristics of theinterval clusters (184). In some examples, arrhythmia analyzer 98 maydetermine if there are primarily two distinct clusters, and whether ornot there if a single transition in time between one cluster andanother. If there is a single transition this may be evidence that TWOSnot present as such a transition may indicate a transition to VT or VF.Arrhythmia analyzer 98 may also determine if there are two distinctclusters. Arrhythmia analyzer 98 may determine there are two distinctclusters if the difference in the mean value of the clusters is greaterthan 150 ms. In addition there should be at least two switches betweenthe clusters in the cardiac episode.

Arrhythmia analyzer 98 evaluates the total evidence for and against afinding of TWOS (186). In some examples, the evaluation of the totalevidence is a probabilistic determination with each possible piece ofevidence having a predetermined weight for or against a determination ofTWOS.

For example, if arrhythmia analyzer 98 determined at the intervallengths where clustered into two distinct bands, then 4 points may beadded to evidence for TWOS. If there is at least one run withalternating intervals for the length of the run, then 1 point is addedto evidence for TWOS, and if there is not a run with alternatingintervals for the length of the run, than 1 point is added to evidenceagainst TWOS. If the longest run of alternating intervals is greaterthan or equal to five intervals and the maximum count for alternatinginterval lengths is greater than or equal to 3 intervals withalternating lengths, then 3 points are added to evidence for TWOS. Ifthe longest run of alternating widths is greater than four intervalswith alternating widths, then 2 points may be added to evidence forTWOS. If the cumulative count of alternating intervals is greater thanor equal to eight alternating intervals, and the cumulative count foralternating widths interval lengths is greater than or equal to six,then 2 points may be added to evidence for TWOS. If the short intervalcluster(s) were found to sum to the long interval cluster, than 2 pointsmay be added to evidence of TWOS. On the other hand, if the shortintervals were found to not sum to the long interval cluster, than 1point may be added to evidence against TWOS. In some examples othercharacteristics, such as slew rate may also be used as evidence for TWOSif the characteristic alternates.

Arrhythmia analyzer 98 may also determine a modality for the cardiacepisode based on number of and characteristics of RR interval lengthclusters. A cardiac episode may be considered unimodal, bimodal,multimodal or too diverse. Modality may be used to confirm or deny acategorization as TWOS and other types of VOS. If there is a singletight cluster of RR interval lengths, then the episode may be consideredunimodal. If there is more than one cluster, arrhythmia analyzer 98identifies the cluster with the highest number of intervals in it. Ifthat cluster has less than one quarter of all intervals in the cardiacperiod, then there is no prominent cluster, and the modality is set to0. If the cluster with the highest number of intervals has more than athreshold amount, e.g., 80%, of all the intervals of the cardiacepisode, the cardiac episode is considered unimodal. If the two clusterswith the highest counts together comprise more than a threshold amount,e.g., 75%, of all intervals in the cardiac episode, then arrhythmiaanalyzer 98 determines the cardiac episode is bimodal. Otherwise, ifthere is more than one cluster, arrhythmia analyzer 98 determines thecardiac episode is multimodal. A unimodal episode may be identified asTWOS and the modality may be set at 1. A bimodal episode may also beidentified as TWOS. A multimodal episode with a mode of 3 may be anindication of oversensing including far-field R-waves or during cardiacresynchronization therapy. 4 or more modes may indicate that theintervals are too diverse to classify as a pattern of oversensing.

FIG. 9 illustrates example an example EGM signal 230 and marker channel236 with characteristics used to detect TWOS. The EGM signal 230includes beats with alternating slew rates 232. The beats with thealternating slew rates may be identified by the dashed circles. The EGMsignal also includes beats with alternating morphology 234. The beatswith the alternating morphology may be identified by the solid circles.Marker channel 236 includes consecutive intervals whose sum equals athird VV interval 238. The intervals are indicated by boxes. Markerchannel 238 also includes alternating VV interval lengths 240 indicatedby arrows.

FIG. 10 is a flow chart illustrating an example method of categorizing acardiac episode including atrial sensing issues. Although described asif implemented by arrhythmia analyzer 98, in some examples, the methodmay be implemented by processor 70 or episode classifier 80 of IMD 16.

According to the illustrated example, arrhythmia analyzer 98 receivesEGM data for a cardiac episode (190). Arrhythmia analyzer 98 determinesif the EGM data indicates the presence of atrial sensing issues (192).In some examples, arrhythmia analyzer 98 determines the presence ofatrial sensing issues if the number of atrial sensed events is differenton the near-field (NF) channel then on the far-field channel. In someexamples, arrhythmia analyzer 98 determines that atrial sensing issuesare present based on irregularity in AA intervals.

If there are not sensing issues, then arrhythmia analyzer 98 continuesto analyze the EGM data using an episode classifier algorithm todetermine whether there the cardiac episode is VT/VF or SVT (194). Ifarrhythmia analyzer 98 determines there is an atrial sensing issue, thenarrhythmia analyzer 98 determines whether the atrial sensing issue isrepairable (196). If there the sensing issue is repairable, for examplebecause it is on only one channel, then arrhythmia analyzer 98 fixes thesensing issue in the EGM signal data and uses an episode classifieralgorithm to determine if the cardiac episode is VT/VF or SVT (194). Ifthe atrial sensing issues are not repairable, then arrhythmia analyzer98 uses classification rules that do not require good atrial sensing(198). In some examples, the classification rules that do not requiregood atrial sensing may include whether RR intervals are extremelyregular or fast, whether AF characteristics are displayed, whether therhythm after pacing VT/VF, the frequency of the ventricular signal, andventricular morphology rules.

In some examples, the rules for RR intervals being extremely regular orfast may be different than those used by arrhythmia analyzer 98 todetermine whether VOS is present. In some examples, arrhythmia analyzer98 categorizes the cardiac episode as VT/VF is any of the followingcriteria related to the RR intervals of the episode being regular andfast are met:

-   -   at least 10 intervals in a row that are with any change between        each consecutive interval less than 40 ms and the absolute value        difference between the intervals is less than or equal to a        threshold factor of 14    -   at least 10 consecutive intervals, the absolute value of the        differences between the intervals is less than or equal to a        threshold factor of 25 and a median VV interval length of less        than 270 ms.    -   If the median VV interval length prior to detection or diagnosis        is less than 200 ms.

Although specific thresholds values are disclosed, other cut-offs ormethods of determining regularity may be used. In general moreregularity is expected as the rate increases. If the cardiac episodedoes not meet any of the criteria for being considered regular or fast,then arrhythmia analyzer 98 determines if the cardiac episode includesatrial fibrillation (AF) characteristics. In some examples, thedetermination of whether AF characteristics are present is based on themethod of FIG. 10, discussed below. If arrhythmia analyzer 98 determinesthat AF characteristics are present, then the cardiac episode isclassified as SVT.

If there cardiac episode does not display AF characteristics, thenarrhythmia analyzer 98 determines if the rhythm after pacing is VT/VF.If the rhythm after pacing is VT/VF then the cardiac episode isclassified as VT/VF. If the cardiac episode is not classified based onthe after pacing rhythm, then arrhythmia analyzer 98 may classify thecardiac episode as VT/VF based on a low-frequency ventricular signal. Insome examines, arrhythmia analyzer 98 may determine the mean frequencycontent of the ventricular signal. The cutoff to be determined VT/VF maybe around approximately 6 Hz. In some examples, a cardiac episodeincluding a period of ventricular pacing may additionally oralternatively be classified based on an analysis of the arrhythmia afterpacing.

Arrhythmia analyzer 98 may also use ventricular morphology rules toclassify the cardiac episode as VT/VF or SVT. In some examples, theventricular beats, e.g., R-waves, in the cardiac episode may be comparedto one or more templates. For example, the beats may be compared to a VTtemplate and to a SVT template. If a predetermined percentage of thebeats in the cardiac episode are found to match one of the templates,then the cardiac episode is classified as either VT/VF or SVT. In theevent that none of the rules result in a classification of the episode,the cardiac episode is classified as unknown or indeterminate.

FIG. 11 is an example method of classifying a cardiac episode asincluding atrial fibrillation (AF). Although described with respect toarrhythmia analyzer 98 in an external device, processor 70 or episodeclassifier 80 of IMD 16 may use similar characteristics to classify acardiac episode as AF at the time of diagnosis.

According to the example method, arrhythmia analyzer 98 receives EGMsignal data for a cardiac episode (200). Arrhythmia analyzer 98 thenidentifies the last 10 ventricular beats prior to diagnosis by IMD 16(202). Arrhythmia analyzer 98 then scrutinizes the 10 beats to determinewhether the last 10 ventricular beats include any of the followingcharacteristics:

-   -   1. There is greater than or equal to 1 very long atrial interval    -   2. The amplitude of any atrial sensed event is very small    -   3. There are some very fast AA intervals    -   4. There is a very long atrial interval spanning detection by        IMD 16 and several good atrial sensed events prior to the long        interval    -   5. The VV intervals are irregular (204).

In some examples, an interval is considered very long if the length ofthe interval is greater than approximately 1800 ms. The amplitude of asensed atrial event may be considered very small is the amplitude isless than approximately 2 millivolts. In some examples, the cardiacepisode is considered to have fast AA intervals if at least 3 of the 10intervals have an interval length below a predetermined threshold. Thethreshold may be approximately 200 ms, for example.

Arrhythmia analyzer 98 determines if the cardiac episode is AF based onthe characteristics (206). In some examples, if 3 out of the 5characteristics are met, then the cardiac episode is considered to beAF. In some examples, arrhythmia analyzer 98 may determine that there iseven a higher likelihood of the cardiac episode is AF if there is normalatrial sensing at termination of the episode. Normal atrial sensing maybe defined as at least 5 good atrial sensed events at termination and noatrial intervals greater than 1800 ms in the last 5 atrial beats. Insome examples, if 2 out of 3 of characteristics 1-3 are found orcharacteristic 5 is true in addition to normal atrial sensing attermination, then the cardiac episode may be categorized as AF.

FIG. 12 illustrates an example EGM signal and marker channel showing AFcharacteristics. The EGM signal includes portions with low atrialamplitude 242. The signal also include a long period with not atrialsensing 244, irregular VV interval lengths 248, and very fast atrialintervals 246. The characteristics shown in FIG. 12 may be used in theexample method of FIG. 11 to determine if a cardiac episode includes AF.

FIG. 13 is a flow chart illustrating an example method of classifying ahigh-rate rhythm arising after pacing. In some examples, the high-raterhythm arises within a predetermined number of beats of pacing. In someexamples the high-rate rhythm arises immediately after pacing. Thepacing may be, for example, cardiac resynchronization therapy (CRT).

An external device such as programmer 24 receives a transmissionincluding EGM data from IMD 16 (210). Arrhythmia analyzer 98 mayidentify episodes in the transmission with diagnoses of tachyarrhythmiain the presence of pacing (212). Arrhythmia analyzer 98 may examineidentified episodes to determine whether a particular episode has eitheran atrial paced-ventricular sensed or an atrial paced-ventricular pacedpattern and a rapid ventricular rate (214) followed by detection anddiagnosis as VT/VF. Arrhythmia analyzer 98 may then classify as VT/VF orSVT based on the characteristics of the EGM signal (216) of the cardiacepisode. Arrhythmia analyzer 98 classifies the cardiac episode as VT/VF(218) and appropriately classified by IMD 16 if: A sensing is totallyabsent during fast V rate (220) prior to detection and atrial pacingsare present at termination. Such a pattern is indicative of an atrialpacing dependent patient. The cardiac episode may also classified asVT/VF if (218) the AA intervals and VV intervals are relatively regularand similar prior to diagnosis and the ventricles transition out of thepacing pattern first (222).

Arrhythmia analyzer 98 may classify the cardiac episode as SVT (224) andimproperly diagnosed by the IMD if AF characteristics existpre-diagnosis. In some examples, arrhythmia analyzer 98 may determine ifAF characteristics exist based on the method of FIG. 11. Arrhythmiaanalyzer 98 may classify the cardiac episode as SVT (224) if the AAintervals and VV intervals are relatively regular and similar prior todiagnosis and the atria leads the rhythm change (228) after pacing.

The techniques described in this disclosure may be implemented, at leastin part, in hardware, software, firmware, or any combination thereof.For example, various aspects of the techniques may be implemented withinone or more microprocessors, digital signal processors (DSPs),application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or any other equivalent integrated or discretelogic circuitry, as well as any combinations of such components,embodied in programmers, such as physician or patient programmers,stimulators, or other devices. The terms “processor,” “processingcircuitry,” “controller” or “control module” may generally refer to anyof the foregoing logic circuitry, alone or in combination with otherlogic circuitry, or any other equivalent circuitry, and alone or incombination with other digital or analog circuitry.

For aspects implemented in software, at least some of the functionalityascribed to the systems and devices described in this disclosure may beembodied as instructions on a computer-readable storage medium such asrandom access memory (RAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), electrically erasable programmable read-onlymemory (EEPROM), FLASH memory, magnetic media, optical media, or thelike. The instructions may be executed to support one or more aspects ofthe functionality described in this disclosure.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method for determining whether T-waveover-sensing (TWOS) occurred during a cardiac episode comprising aplurality of sensed beats, the method comprising: identifying at leastone beat run of at least a predetermined number of consecutive ones ofthe beats during the cardiac episode, wherein each of the beats withinthe run have at least one characteristic that alternates from beat tobeat; clustering the beats into two or more clusters based onbeat-to-beat interval length; and determining, based on at least one ofthe runs and the clusters, whether TWOS occurred during the cardiacepisode.
 2. The method of claim 1, further comprising determiningwhether the identified beat run comprises alternating interval lengthsfor at least a portion of the beat run.
 3. The method of claim 1,further comprising determining whether the identified beat run comprisesalternating widths for at least a portion of the beat run.
 4. The methodof claim 1, further comprising determining whether the identified beatrun comprises alternating morphologies for at least a portion of thebeat run.
 5. The method of claim 1, further comprising determiningwhether the identified beat run comprises alternating amplitudeintervals for at least a portion of the beat run.
 6. The method of claim1, wherein the determination of TWOS is a probabilistic determination.7. The method of claim 6, wherein evidence for TWOS comprises intervallengths clustered into two distinct bands; at least one beat run withalternating intervals for the length of the beat run; a run ofalternating intervals greater than or equal to five intervals and atotal count for alternating interval lengths is greater than or equal tothree; and a run of alternating widths is greater than four intervals;wherein at least interval lengths clustered into two distinct bands anda run of alternating widths greater than four are given differentweights.
 8. The method of claim 1, further comprising determiningwhether there is a single transition between two distinct clusters, and,in response to a single transition between two distinct clusters addingweight to evidence of no TWOS.
 9. The method of claim 1, whereinclustering the beats into two or more clusters comprises clustering thebeats into a first cluster, a second cluster, and a third cluster basedon beat-to-beat interval length, the method further comprising:determining a representative interval length for each of the threeclusters, wherein the first cluster interval length is longer than thesecond cluster interval length and the first cluster interval length islonger than the third cluster interval length; summing the secondcluster interval length and the third cluster interval length; comparingthe sum of the second cluster interval length and the third clusterinterval length to the first cluster interval length; and adding weightto evidence of TWOS in response to the sum of the second clusterinterval length and the third cluster interval length beingapproximately equal to the first cluster interval length.
 10. The methodof claim 9, wherein the first cluster interval length is a mean intervallength for each of the beats in the first cluster.
 11. The method ofclaim 1, wherein each of the clusters is separated by a zone of intervallengths in which there is no more than one interval.
 12. The method ofclaim 11, wherein the dead zone is between approximately 5 ms and 25 ms.13. The method of claim 1, further comprising determining a count of atotal number of alternating beat runs within the cardiac episode, and inresponse to the count being above a predetermined threshold, addingweight to evidence of TWOS.
 14. The method of claim 1 further including:determining mean values of each of the clusters; determining adifference between the mean values; and determining whether the at leasttwo clusters are distinct clusters based at least in part on thedifference between the mean values of each of the two clusters.
 15. Themethod of claim 1, further including determining a number of alternatinginterval lengths for the at least one beat run.
 16. A system fordetermining whether T-wave over-sensing (TWOS) occurred during a cardiacepisode comprising a plurality of sensed beats, the system comprising: aprocessor configured to: identify at least one beat run of at least apredetermined number of consecutive ones of the beats during the cardiacepisode, wherein each of the beats within the run have at least onecharacteristic that alternates from beat to beat; cluster the beats intotwo or more clusters based on beat-to-beat interval length; anddetermine, based on at least one of the runs and the clusters, whetherTWOS occurred during the cardiac episode.
 17. The system of claim 16,wherein the processor is further configured to determine whether theidentified beat run comprises alternating interval lengths for at leasta portion of the beat run.
 18. The system of claim 16, wherein theprocessor is further configured to determine whether identified beat runcomprises alternating widths for at least a portion of the beat run. 19.The system of claim 16, wherein the processor is further configured todetermine whether the identified beat run comprises alternatingmorphologies for at least a portion of the beat run.
 20. The system ofclaim 16, wherein the processor is further configured to determinewhether the identified beat run comprises alternating amplitudeintervals for at least a portion of the beat run.
 21. The system ofclaim 16, wherein the processor is further configured to perform aprobabilistic determination of whether TWOS occurred.
 22. The system ofclaim 21, wherein evidence of TWOS comprises interval lengths clusteredinto two distinct bands; at least one beat run with alternatingintervals for the length of the beat run; a run of alternating intervalsgreater than or equal to five intervals and a total count foralternating interval lengths is greater than or equal to three; and arun of alternating widths is greater than four intervals; and whereinthe processor is further configured to give different weights to atleast interval lengths clustered into two distinct bands and a run ofalternating widths greater than four.
 23. The system of claim 16,wherein the processor is further configured to determine whether thereis a single transition between two distinct clusters, and in response toa determination of a single transition, the processor is configured addweight to evidence of no TWOS.
 24. The system of claim 16, wherein theprocessor is further configured to: cluster the beats into a firstcluster, a second cluster, and a third cluster based on beat-to-beatinterval length; determine an interval length for each of the threeclusters, wherein the first cluster e interval length is longer than thesecond cluster interval length and the first cluster interval length islonger than the third cluster average interval length; sum the secondcluster interval length and the third cluster interval length; comparethe sum of the second cluster interval length and the third clusterinterval length to the first cluster interval length; and add weight toevidence of TWOS in response to the sum of the second cluster intervallength and the third cluster interval length being approximately equalto the first cluster interval length.
 25. The system of claim 24,wherein the first cluster interval length is a mean interval length foreach of the beats in the first cluster.
 26. The system of claim 16,wherein the processor is further configured to separate each of theclusters by a dead zone.
 27. The system of claim 16, wherein theprocessor is further configured to determine a count of a total numberof alternating beat runs within the cardiac episode, and in response tothe count being above a predetermined threshold, add weight to evidenceof TWOS.
 28. The system of claim 16, wherein the processor is furtherconfigured to: determine the mean values for each of the clusters;determine a difference between the mean values of each of the twoclusters; and determine whether the at least two clusters are distinctclusters based at least in part on the difference between the meanvalues of each of the two clusters.
 29. The system of claim 16, whereinthe processor is further configured to determine a number of alternatingintervals for the at least one beat run.
 30. A non-transitorycomputer-readable medium comprising instructions for causing aprogrammable processor to determine whether T-wave over-sensing (TWOS)occurred during a cardiac episode comprising a plurality of sensedbeats, the instructions causing the programmable processor to: identifyat least one beat run of at least a predetermined number of consecutiveones of the beats during the cardiac episode, wherein each of the beatswithin the run have at least one characteristic that alternates frombeat to beat; cluster the beats into two or more clusters based onbeat-to-beat interval length; and determine, based on at least one ofthe runs and the clusters, whether TWOS occurred during the cardiacepisode.
 31. A system for determining whether T-wave over-sensing (TWOS)occurred during a cardiac episode comprising a plurality of sensedbeats, the system comprising: means for identifying at least one beatrun of at least a predetermined number of consecutive ones of the beatsduring the cardiac episode, wherein each of the beats within the runhave at least one characteristic that alternates from beat to beat;means clustering the beats into two or more clusters based onbeat-to-beat interval length; and means determining, based on at leastone of the runs and the clusters, whether TWOS occurred during thecardiac episode.
 32. A system for determining whether T-waveover-sensing (TWOS) occurred during a cardiac episode comprising aplurality of sensed beats, the system comprising: a processor configuredto: cluster the beats into two or more clusters based on beat-to-beatinterval length; and determine, based on the clusters, whether TWOSoccurred during the cardiac episode.